T cell related osteoimmunology in fracture healing: Potential targets for augmenting bone regeneration

Journal of Orthopaedic Translation, Journal Year: 2025, Volume and Issue: 51, P. 82 - 93

Published: Feb. 4, 2025

Language: Английский

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Using artificial intelligence-based software for an unbiased discrimination of immune cell subtypes in the fracture hematoma and bone marrow of non-osteoporotic and osteoporotic mice

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 8, 2025

Abstract It is well established that the early inflammatory response following fracture essential for initiating subsequent bone regeneration. An imbalance in inflammation, whether within innate or adaptive immune response, can result impaired healing. In our previous studies, we demonstrated that, example, mice with ovariectomy-induced osteoporosis exhibit altered cell populations hematoma and marrow, leading to delayed These analyses were conducted using conventional FACS/flow cytometry software, where surface marker expression was assessed a single threshold based on isotype controls—a binary “yes no” decision. Recent advances have highlighted are often more heterogeneous, distinct phenotypic subgroups depending their polarization status. This has been particularly documented macrophage subpopulations (M1, M2, intermediate states). light of this, employed commercially available artificial intelligence-based clustering software (Cytolution) accurately objectively identify subpopulations. We re-analyzed flow raw data from marrow non-osteoporotic osteoporotic at day 1 after fracture. Our findings revealed subclusters granulocytes (27 subclusters), macrophages (7 B cells (4 T (6 subclusters) marrow. Comparing mice, observed an increased abundance specific subpopulation alongside significant reduction particular granulocyte hematoma. Several granulocytes, cells, also The role these remains be investigated future. results suggest AI-based may provide powerful tool identifying phenotypes during regeneration, offering nuanced understanding data.

Language: Английский

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Using artificial intelligence-based software for an unbiased discrimination of immune cell subtypes in the fracture hematoma and bone marrow of non-osteoporotic and osteoporotic mice

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0322542 - e0322542

Published: April 29, 2025

It is well established that the early inflammatory response following fracture essential for initiating subsequent bone regeneration. An imbalance in inflammation, whether within innate or adaptive immune response, can result impaired healing. In our previous studies, we demonstrated that, example, mice with ovariectomy-induced osteoporosis exhibit altered cell populations hematoma and marrow, leading to delayed These analyses were conducted using conventional FACS/flow cytometry software, where surface marker expression was assessed a single threshold based on isotype controls—a binary “yes no” decision. Recent advances have highlighted are often more heterogeneous, distinct phenotypic subgroups depending their polarization status. This has been particularly documented macrophage subpopulations (M1, M2, intermediate states). light of this, employed commercially available artificial intelligence-based clustering software (Cytolution) accurately objectively identify subpopulations. We re-analyzed flow raw data from marrow non-osteoporotic osteoporotic at day 1 after fracture. Our findings revealed subclusters granulocytes (27 subclusters), macrophages (7 B cells (4 T (6 subclusters) marrow. Comparing mice, observed an increased abundance specific subpopulation alongside significant reduction particular granulocyte hematoma. Several granulocytes, cells, also The role these remains be investigated future. results suggest AI-based may provide powerful tool identifying phenotypes during regeneration, offering nuanced understanding data.

Language: Английский

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Extracellular vesicle-mediated crosstalk in tumor microenvironment dominates tumor fate

Trends in Cell Biology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 1, 2024

Language: Английский

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